Craving Induction and Treatment Response in Transcranial Magnetic Stimulation for Tobacco Use Disorder
Bibliographic record
Abstract
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an emerging treatment for several mental health conditions including substance use disorders. Symptom provocation before rTMS is hypothesized to activate disease-related neurocircuitry and enhance treatment effects. For addiction, craving induction is frequently used before treatment, but its clinical utility requires further exploration. To assess whether craving induction enhances treatment outcomes, we analyzed data from the pivotal multisite trial of deep TMS (DTMS) for tobacco use disorder (TUD). METHODS: A total of 262 participants were randomized to 6 weeks of active or sham DTMS and instructed to set a quit date within the first 2 weeks. Craving was assessed using a 10-point visual analog scale before and after craving induction across 18 treatment sessions. Mixed-effects models tested whether craving induction increased craving across sessions and whether its magnitude predicted end-of-trial 4-week continuous quit rate (4W-CQR). RESULTS: Craving induction produced small but significant increases in craving (b = 0.34, 95% CI 0.18 to 0.50, p < .05) without evidence of habituation across sessions (b = -0.01, 95% CI -0.02 to 0.50, p = .38). Change in craving after cue induction did not significantly predict end-of-trial 4W-CQR, and there was no interaction with study arm (p = .95). CONCLUSIONS: Craving induction reliably increased craving, although the magnitude of cue-induced craving was not associated with end-of-trial smoking cessation. This finding suggests that greater craving induction does not predict greater therapeutic effect of rTMS. Further research is needed to understand the mechanisms and clinical implications of rTMS and brain state in TUD, including potential improvements in related domains such as inhibitory control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".